食管鳞状细胞癌新辅助化疗放疗反应评估的血管通透性和纹理参数诊断性能数据集

数据集获取（Figshare）

• 源站标题：DataSheet_1_Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma.pdf
• DOI：10.3389/fonc.2021.604480.s001
• 发布日期：2021-05-18
• 文件数：1 个
• 文件总体积：323.80 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Diagnostic_Performance_of_Vascular_Permeability_and_Texture_Parameters_for_Evaluating_the_Response_to_Neoadjuvant_Chemoradiotherapy_in_Patients_With_Esophageal_Squamous_Cell_Carcinoma_pdf/14611623

命令行下载

curl -L -o "DataSheet_1_Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Pat.pdf" "https://ndownloader.figshare.com/files/28055352"

文件清单

• DataSheet_1_Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Pat.pdf（323.80 KB）

文件总体积 323.80 KB（源站 API 实测）。

数据加载示例（表格/文本类）

import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))

数据说明

BackgroundEsophageal squamous cell carcinoma (ESCC) is an aggressive type of cancer, associated with poor prognosis. The development of an accurate and non-invasive method to evaluate the pathologic response of patients with ESCC to chemoradiotherapy remains a critical issue. Therefore, the aim of this study was to assess the importance of vascular permeability and texture parameters in predicting the response to neoadjuvant chemoradiotherapy (NACRT) in patients with ESCC.
MethodsThis prospective analysis included patients with T1–T2 stage of ESCC, without either lymphatic or metastasis, and distant metastasis. All patients underwent surgery having received two rounds of NACRT. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) twice, i.e., before the first NACRT and after the second NACRT. Patients were assessed for treatment response at 30 days after the second NACRT. Patients were divided into the complete response (CR) and partial response (PR) groups based on their responses to NACRT. Vascular permeability and texture parameters were extracted from the DCE-MRI scans. After assessing the diagnostic performance of individual parameters, a combined model with vascular permeability and texture parameters was generated to predict the response to NACRT.
ResultsIn this study, the CR and PR groups included 16 patients each. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (ve), and entropy values, as well as changes to each of these parameters, extracted from the second DCE-MRI scans, showed significant differences between the CR and PR groups. The area under the curve (AUC) of Ktrans, ve, and entropy values showed good diagnostic ability (0.813, 0.789, and 0.707, respectively). A logistic regression model combining Ktrans, ve, and entropy had significant diagnostic ability (AUC=0.977).
ConclusionsThe use of a combined model with vascular permeability and texture parameters can improve post-NACRT prognostication in patients with ESCC.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2021.604480.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Diagnostic_Performance_of_Vascular_Permeability_and_Texture_Parameters_for_Evaluating_the_Response_to_Neoadjuvant_Chemoradiotherapy_in_Patients_With_Esophageal_Squamous_Cell_Carcinoma_pdf/14611623）。